Resources of our survey paper "Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies"

[ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time

Benchmarking machine learning inferencing on embedded hardware.

Source code of “Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing” (CAIAC 2025)

Epsilon is a library with functions for machine learning and statistics written in plain C. It is intended to run on microcontrollers.

MINERVA - Minimal Inference Engine for Robust, Verifiable, and Authenticated ML. Encrypted, integrity-verified neural network inference for MCUs down to ATmega328P.

This project focuses on the implementation of optimized Linear and DNN regression models for inter-vehicle distance prediction in a Cooperative Adaptive Cruise Control (CACC) application. It leverages Tensorflow Lite to create optimized models through quantization and pruning for realtime inferencing on Raspberry Pi and On-board Unit (OBU) of Co…

Implementación de un clasificador de vocales basado en SVMs y features MFCC.

Real-time wildfire detection on microcontrollers with TinyML + Edge Impulse + Wokwi

Experimental pipeline for evaluating motion-related decisions in TinyML-based inertial systems under embedded constraints. The project focuses on understanding how IMU-only models behave, fail, and degrade when deployed on resource-limited hardware, using controlled datasets and reproducible engineering workflows.

A new communication paradigm proposal for restricted bandwidth and fragile channel conditions in NTN.

A Smart Mask Enforcement System using Multitenant Cascading Architecture in TinyML

Tiny CNN cat/dog classifier for RISC-V edge AI, with PyTorch training, quantized firmware export, and Renode simulation benchmarks.

Edge-optimized wildlife detection for crop protection

Tiny implementation of kernel passive-aggressive regression on a budget in C.

Ingenuity is an optimized inference engine and benchmarking tool for TinyML models on embedded IoT devices.

Embedded Software: Running machine learning models on Raspberry Pi

Practical work developed for the subject internet of things/embedded systems. My Replenisher is a complete end-to-end application, ranging from TinyMl with arduino nano 33 ble, communication with ESP32 to a mobile application that embodies the entire final scope.

The official Edge Impulse firmware for PSoC63 (CY8CKIT-062-BLE)

Tiny ML für Fahrbahntyp-Erkennung